1. Technical Field
The present invention relates to target tracking, and more particularly, to a method for tracking a target in an image which employs a block matching algorithm and a device therefor.
2. Related Arts
In general, in an automatic target tracking method which is used in radar signal processing, a motion of a target is automatically tracked, in order to make a decision based on the tracking data.
The automatic target tracking may look similar to an image compression method used in video equipment. However, only the motion of a predetermined target is relevant in the automatic target tracking while all objects in the image are relevant in the image compression method.
One conventional automatic target tracking method is a centroid tracking method using the center of gravity of a target. In the centroid tracking method, an image signal value of each pixel is compared with a predetermined threshold, and pixels are divided into target pixels and background pixels according to the comparison result to calculate the center of gravity of the pixel corresponding to the target. If B(h,v,m) indicates a binary image signal in which a value of the pixel corresponding to the target is one and a value of the pixel corresponding to the background is zero, the center of gravity of the target is expressed by: ##EQU1##
In such a centroid tracking method, it is difficult to accurately calculate the center of gravity of the target if the background pixels are determined to be target pixels or vice versa. However, it is difficult to divide pixels in the image signal into a target pixels and background pixels since the image signal generally includes noise and the background changes. Accordingly, when the centroid tracking method is applied, it is difficult to repeatedly and accurately track the target.
Another conventional automatic target tracking method is a correlation coefficient tracking method. Here, a displacement of a moving target pixel is calculated from the correlation of temporal/spatial intensity differences of each pixel between two consecutive frames.
That is, when I(h,v,m) indicates an intensity function value of the pixel (h,v) in m-th frame, horizontal/vertical and temporal gradients which indicate intensity differences in a pixel unit is expressed by: ##EQU2##
Displacements Dh and Dv of a moving target in horizontal and vertical directions, respectively, between a (k-1)th frame and a k-th frame, are expressed by equation (3) by use of correlation coefficients of the gradients. ##EQU3##
In the above correlation coefficient tracking method, the temporal gradient is expressed in terms of the spatial gradient by Taylor series expansion. However, the tracking performance is deteriorated with respect to a target moving at high speed in the case that higher-order terms are ignored in calculating the Taylor series expansion. For example, it is difficult to track a target when the speed of the target is 2 pixels or more per frame. Also, when the image includes a background, the tracking performance is deteriorated due to the gradient caused by the background.
Still another automatic target tracking method is a block matching algorithm. In this method, a template including a target is formed in a frame, and a subsequent frame is searched in order to determine where the information included in the template of the previous frame is located in the subsequent frame. For the purpose, candidate templates are sequentially formed in a predetermined searching area of the subsequent frame to calculate the similarity between each candidate template and the template of the previous frame. Then, the position difference between the candidate template showing the maximum similarity and the template of the previous frame is determined as the displacement of the target.
Some measures of the similarity are a normalized correlation coefficient, a mean of absolute difference (MAD), mean of squared differences (MSD), and a normalized invariate moment equation. For example, the mean of absolute difference between the templates is calculated by: ##EQU4##
where s denotes a two-dimensional position vector, d.sub.m denotes a two-dimensional expected displacement of a target, and M denotes the total number of pixels. Here, the expected displacement d.sub.m which results in the minimum of the mean of absolute difference is determined as the displacement of the target.
The block matching algorithm shows a stable performance even when an image signal includes noise. However, in case that the image signal includes a complex background component, the performance is deteriorated since the contribution of the difference in intensity function values of the background increases. Furthermore, it is difficult to implement a real-time system since a great deal of computation is involved.
Devices and methods for detecting and tracking objects are disclosed by U.S. Pat. No. 5,631,697 issued to Nishimura et al. entitled Video Camera Capable Of Automatic Target Tracking; U.S. Pat. No. 3,955,046 issued to Ingham et al. entitled Improvements Relating To Automatic Target Following Apparatus; U.S. Pat. No. 5,574,498 issued to Sakamoto et al. entitled Target Tracking System; U.S. Pat. No. 5,583,947 issued to Florent entitled Device For The Detection Of Objects In A Sequence Of Images; U.S. Pat. No. 4,644,405 issued to Roy et al. entitled Method And Apparatus For Scanning A Window In The Image Zone Of A Charge Transfer Device Of The Frame Transfer Type; U.S. Pat. No. 5,706,362 issued to Yabe entitled Image Tracking Apparatus; U.S. Pat. No. 5,757,422 issued to Matsumura entitled Tracking Area Determination Apparatus And Object Tracking Apparatus Utilizing The Same; and U.S. Pat. No. 5,729,338 issued to Houlberg et al. entitled Computer Controlled Optical Tracking System.
Although several devices and methods currently exist for tracking targets, I have discovered that an enhanced target tracking method and device would be desirable, in order to increase efficiency.